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@sarchak
Created September 11, 2017 04:34
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to linear regression.\n",
"\n",
"In this jupyter notebook we will start with a very simple problem of predicting the height of the user using the weight, age and sex. \n",
"\n",
" * Simple linear model\n",
" * Linear model with non linear interactions\n",
" * Random Forest\n",
" * GridSearch to find the best paramets"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from matplotlib import style\n",
"style.use('fivethirtyeight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the data and learn about existing fields and analyze the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('dataset/Howell1.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male\n",
"0 151.765 47.825606 63.0 1\n",
"1 139.700 36.485807 63.0 0\n",
"2 136.525 31.864838 65.0 0\n",
"3 156.845 53.041915 41.0 1\n",
"4 145.415 41.276872 51.0 0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Visualize the data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110f60e10>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Cb4tWFFdbSY/VIDastToJG20oflalFxd2LGyEqNKzse5DT5qlql4fZoMVvdgt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\n1q2YO3eucB7JTcqePXuwc+dObN26FQMHDsSZM2e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"text/plain": [
"<matplotlib.figure.Figure at 0x110f60400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.plot(x='age', y='height', kind='scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit Regression Line"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
" \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x110ff4e10>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JZs9caUFta6rCl/90LWJhGW/8rtfTLrF7MIUdb54DAAgCD50AIARb77oGa1YU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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114f69320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.lmplot(x='age',y='height',data=data,fit_reg=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see from the data that height of a person increase from age 0 to 20 but tend to stabilize after 20 years. During this phase we can see that its easier to fit a linear model but after that the data doesn't signify any linear interaction between age and height\n",
"\n",
"A linear model would not perform well on all the dataset. But if we consider only the data in the range age 0 to 20 we can linear model does a better job of fitting the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit a linear model for age < 20"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x114fbd898>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eupnSMpUuS4r1ic+fTxYowiVKjnwPbnKtPe3xhvXT/MTxNd39YVz0hvDSvnN4\n7/hF/fo1l9bg86sa8PAvDyWdutDZF0qyWnzybe2lJovMkOASJUW+jQ/51J4aRR4dg6f5wKAJjS8C\nU7kFT7/8qV4jKwocbrtmLpYvqILLbkq5B8ZS+3jVVdrR2ukbvV6AHC6QX2tvsfPnUxESXGJSk2u0\nmu/BTX73DcWpcbFUGcOFvjCUwRayMpsRd29sxpxaJ1x2E3ieg81igD/JpIhUUxsA4KarGpL62t60\nuiHlPRNBsfLnUxUSXGLSkk+0mu/BTT73jZykCwYoKhB3nG2ocWDLDU2oLrfCaR0ymVk6rwL7E3wR\nEtdTsWROBe7a2EzR4xSHBJeYtOQTdebb+JDPfZUuCzr7Q3DxJviCMQQjsv6zVYurcfPqBpQ7zfrI\n8jiSrMJlN8IfkqAyBp7j4LAasvJFIIGd2lCVAjFp6fGG0esN43yXH21dfpzv8qPXG04bdebb+JDP\npNrlCysRk7RqhESxXXNpDb50zVxUua2jxDb+vkwGASajAIPIw2QUYDIIVD41DaAIl5i0+EPDo0YG\nIBiR4Q9FU94zloObbEfRxJsZeI5HMBzTDWhEgcOmlfVYt2yWnq9NhlEU0OEfKueKT3xwOVIfqBGl\nAQkuMWnxBmI5rcfJ56t3tpNq42PLPzh5Ec/vPQtJS9qirtKGuzYuQG3F8HxtclJIeZoqBaI0oJQC\nMWlRkpnFplkfC9kcmkmygh5vBC8fOIfn9pzWxdZtN4IxhtfeO4/z3ZlLqGKymtQXISaT4JY6BY1w\nP/74Yzz22GPYtm0bjh07hkceeQSCIMBoNOJf//VfMWPGDOzYsQPPPfccRFHEPffcg3Xr1hVyS8QU\nwiDwSQ+SDML4xwmZDs3CURnd/SE89/opnPxsAIA25sZpNcJsEiEIPHp90axqfo0ij3BEhsBzEAZL\ny8IRGUZ3ejcucuKa+hQswn366afx/e9/H9Golm979NFH8dBDD2Hbtm3YsGEDnn76afT09GDbtm14\n7rnn8Mtf/hKPP/44YrH0XxeJ6cOKRVU5rY+FdIdtvlAMJ9u9+MkLLbrYWs0iGmscsFsNMIj8sOGO\n6SbiaqQQ1jT2h+TEVRoUTHDr6+vx5JNP6o8ff/xxLFq0CACgKApMJhOOHDmCZcuWwWg0wuFwoL6+\nHsePHy/UlogJpKW1Dz9/sQWP/OY9/PzFlqyE4hs3LcZVl9Zo7lWc1gF21aU1+MZNi8d9f0vmVODL\n185Ftdu6hEAcAAAgAElEQVQCnudQ7bbgtqvnYGaFDe8fv4if/akFfT4tvVBbYcW3brtUN3YZOUk3\nU7VBvH5XHHxfosjD7TANm/4wknQlcsTUoWAphU2bNqG9vV1/XFWlRSUffvghtm/fjmeeeQZvv/02\nHA6Hfo3NZkMgkHlkiNtthThG4+XKSkfmi4rAdNjHhycu4qX95wAAgsCj3x/FS/vPoazMiisWDI9W\nR+7jga+tKti+RrKu0oF1KxsBxPO1Ybz01lns2t+qX7NicTX+6sbFqCm34p1jF9HZO/rP68wZ9rSf\nZ121E529AThsww/XRt6X+P+eQCypbaI3GCv4n6Hp8Gc0F8ayj6JWKfz5z3/Gz372M/ziF79AeXk5\n7HY7gsGh8phgMDhMgFPh8YzN5GOyjH6eLvvY9faZpLnYXW+fwezyIYep8drHWHOdNocZn566iB1v\nnMGxNg8A7du+VvI1EyaOwe8LY8WCGXi+a7S/wfIFM9K+j2zuG/lZuO3GpDnmareloL930+XPaD77\nyEd4iya4L774In73u99h27ZtcLlcAIClS5fiiSeeQDQaRSwWw5kzZ9Dc3FysLRFFIpWrVjaF/rmK\n51in9vpCMXzWH8ZP/3RUr1wwGwXcsb4Jl82rgNNm1FMI+db85nPf2qW1Sb0UyIlralEUwVUUBY8+\n+ihqa2tx7733AgBWrFiBv/3bv8XWrVuxZcsWMMZw//33w2Si4u9SI5mrltcfhduevl41H/HM17wm\nbhb+ydk+7HjjDMJRreGiym3B1o0LMKfWAat5tLlMvu22+dyXbWMGMXkpqODW1dVhx44dAIB33303\n6TWbN2/G5s2bC7kNYsLJ/VQeyE888zGhkWQVHn8EbxzuwO53P9OFbHGjG7evm4/qciuMhvEf1pgL\niY0ZI9epNGzqQJ1mRMGJn8qP/Dqc7lQegO5nMPK+dOKZqwlNOCqj1xvGH948i0/ODlVOrL+yDhtW\n1KHCaYbAT3x/EI2vKQ1IcImCExdB84joLJODVz6eA9lOHWCMwR+S0NEbwPbdJ/VpCyaDgL++9RI0\n1TpQlpCvnWgm8/h3Insm/p9uouTJf3R57p4Dw+ppOa2e9svXzh32tVtVGTx+LV/7kz+26GJbUWbG\nPV9cgqsv18xnJovYApN3/DuRGxThEgVnyZwKnOvyY+/hDgTDEmwWA65bNitj7jEmq3A5TKOqGzJ5\nDqQ7kJJkFf3+CPYd6cT/vNOGuC1D82wX7lg/H7XlNjisRkSCqR3JJgIaX1MakOASBaeltQ8fnOiB\nw2qEY9BJ64MTPWiscaQVjEqXBaonPOqgKN+v0eGojL6BCP607yw+PNmrr193+Uz8r5X1KC8zQyyA\nT8N4QQbkU5/J+6eLKBn2HelEeNDcu7MvqNflZmpLHc+v0YGwhPPdfvzi5aO62BpEHnesb8Ita+Zg\nhtsyqcWWKA0owiUKTlu3H94kdbhtGXKk4/E1mjGGgWAMJ8578cyrJxEIa8Mb3Q4T7t7YjPmzyvSo\nmyAKDQkuUXAkWfN8TfSxFXgOkqxkvPdclx+nOwYQCEsYCEZRV2XPWnBlRYU3EMX+T7qw88A5/fXn\nznRiyw1NqK2wDUtXxLvaPIEY3HZjVuJOlolELmQluPfee+8w5y8A+OpXv4rf/OY3BdkUURzyEZjE\n+7IVmZikjjINV1SWsQ5358Fz2DloegMAgZCkP755dWPae6OSgr6BMF7cdw7vHb+or69ZUoMbr2rE\nDKd5mBlMYlebQeSz6mobaxsxMf1IK7jf/va3cezYMVy8eBHr16/X1xVFQU1NTcE3RxSOfARm5H1A\ndiITk5JHsqnW4+w93JFyPZ3ghiISLvQG8cyrp9A2OIFBFDjcdvVcrFpcnXTeWD5dbfm2ERPTl7SC\n+y//8i/wer149NFH8f3vf3/oJlFERQX9gZrK7DvSqXdxKQqDIHBwWAwZxSIfkVEZS9rcyzLM8Irn\nW0cSTLEeb2bY88Fn+J9D5yEr2vNbzSK+9vmFaKpzwWk1JK2vzaeTi7q/iFxJK7h2ux12ux0/+9nP\ncObMGXg8Hv0vyfnz57FixYqibJIYf9q6/fAMHmRxHKd3cWUq9s9HZJw2I3yBGFTGwJhmocBz3Cg/\n2JHYLQYEQqPF1WYZbSITN595cV8r3k74R8Eo8vqhWFma18unk4u6v4hcySqH+/DDD+PNN99EfX29\nvsZxHP77v/+7YBsjCksyf1pA8z1IRz4ic92yWdi5/xz4EXHudctmpX2t+H3J1hORZAW9vij+fOAc\n9rd06etWs4gymxGiyOP94xexfEHq0TzZtgSP9R5iepOV4B44cACvvvoqjEYqnykVkk0P0NbTu2Kt\nXVqL7btPjur+Sicy8XzryE6zTAdf2dwXisjo6g/i2ddO4eyF4abe4YgMWVbgdpgzfs1PLEHzBmOo\ndmc+DKTuLyJXshLc2tpaRKNREtwSoqHaAca0PKmiMIiiJpwN1faM9wbDEsIRGQyAJKmpzBeHcfPq\nxowCm4zGGgfmzyrTKyIaazSXfcYYfCEJZy8MYPvuk3p6JBEGQJIZ+gYiaQ1v4sQ7ufKbLkDutERm\n0gru3//93wPQqhK+8IUvYPny5RCEoQjohz/8YWF3RxSMtUtr0T3YNmsQh8aRZ/o6vOP1UwhFNHPu\nuNCGIjJ2vH4KS74xvpFdqooIVWWYNcOOD0724Pk3h8b3WEwCJFmFrAw/pFMZS2t4M977A6gsjEhO\nWsFduXLlsP8SpUM+X6EBoLMv+aFZqvWxkKwigjGGPR+0o9xpxpsfXdDXr2iege7+EC56wxB4bljd\nL89xGQ1v8t1fstFBVBZGpCKt4N52220AgAsXLgxb5ziORuGUAPl8hU5VypWpxCsfRlZEqCqDJKs4\n1T6ASKwfAMBzwI2rG7Dm0lo8v/cMegeikFVlWITLGIPRMP4+Cfm2LBPTl6xyuN/61rdw6tQpNDc3\ngzGGU6dOobKyEoIg4JFHHsHq1asLvU9ikmAzG5LWxyYr1Ror8YoIxhhUlSEqqej3RfTo1WoSceeG\nJiyqd8NlN+Gay2fik7P9o56H57iCpBRSVXpk07JMTE+yEtzq6mo88sgjWLJkCQDgxIkTeOqpp/Dg\ngw/i29/+Np5//vmCbnIqUqo99htWzsZL+1qhqgwMWh6X5zlsWDE77X35fB5rl9biD3vPQFEZQhEZ\n3kBU183aCivu3tiMmTPsejPDkjkVcDmM8PpjuugZRB5ldlNBUgqpKj2MGSo9iOlLVoLb0dGhiy0A\nLFiwAOfPn0dtbS1UNX0//HSklA9T8inxyvfzaK5zYf2Vs7Dr4PlhVQhL51XgS9fOxQynedQk3YZq\nB8zG4jQjNFQ7AIZRM9fqs6j0IKYnWQnu7Nmz8dhjj+ELX/gCVFXFzp070dDQgMOHD4OfBAP2Jhul\n3mOfa4lXPp9HMCKhzxvBoU8v4uJgowUHYNPKelx7eS1cDjNMSSbpFrMZIV7pMXJWGzU+EKnISnB/\n9KMf4amnnsJ3vvMdCIKA1atX45//+Z/x+uuv4x//8R8LvccpR6n32OeaHsjl82CMwReMob0niG27\nT+hiazYKuGN9ExY3uOFymFKahRezGYEaH4hcyUpw7XY7HnjggVHrt95667hvqBQo5R77fNID2X4e\nsqKizxfBp+c8eG7PKURiWh62ym3B1o0LMHOGDWV2o3YIloZijqKhsTdELqTNB8TLwhYuXIhFixbp\nv+KPieSU8oTVdOmBVGTzeURjCi72h/D6Bx34zV+O62K7uNGNe76wBPXVdrgdpoxiSxCTmbQR7gsv\nvAAAOH78eFE2UyqU8lfNfNIlmT6PQFhCvz+CnQfP4/1j3fp966+sw+wqG3YeaEW/P1pS1R7E9CSr\nlEIsFsN//dd/obW1FQ899BB+/etf45vf/CZ5K6ShVL9q5psuSfZ5xOeNdfYFsX33SXT2hQAARgOP\nzevmw2jg8dr77bplZClVexDTk6xKDP7pn/4JoVAIR48ehSAIaGtrw4MPPljovRGTkLVLa5NO4M01\nXRLP1x4914+fvNCii21FmRn3fHEJLps3A5+c6UMkpuQ87ZcgJitZRbhHjx7FCy+8gLfeegsWiwU/\n+tGPcMsttxR6b8QkZWQWNdesajSmwBuIYH9LF/58sA1x24NL5lbgS1fPgdthQpnNiPMXA9Q6S5QU\nWQkux3GIxWL6VzuPx5NxMgAx+clniOS+I50wm8RRtafZ1hgHwhI8fm0yw4cne/T1ay+fids3LoQc\nlWAfbBOWZBXqYFtvYlcbtc4SU5WsBPev/uqv8PWvfx09PT149NFH8dprr+Fb3/pWofdGFJB8h0jm\nW2OsMoaBQAwXvWE8s/sE2nuC+mt/+dp5uGx+BSpdFvgTPMQZAxRlqCWXQXtcAFsEgigKWQnujTfe\niGAwCI/Hg7KyMnz961+HKGZ1KzFJybcbLp9DM1nRUgFnLvjw7Ksn4R80v3E7TLh7YzNmV9rhcphg\nNolI9CzjOEAQuFERLn25IqYqWanmfffdh56eHsybNw8dHUOjq7/4xS8WbGNEYck3Ul27tBbP7D45\nyj8g1aFZNKZgIBjFoU+78dL+c7rT19yZTtx5QxPKHeaUzQwGkQfPceAFbsQ6mcMQU5OsBPfs2bP4\ny1/+Uui9EEVkLN1wUUlBNKZAZQyKwmBM4ZoVCEvwBqLYdbANhz4dqq+9akkNPv+5BpTZjHq+NhmJ\nY4ASDb6zGQNEEJORrMrC6uvrR5mQE1ObfLvhdh1oQygiQ+A5GAQeAs8hFJGx62Cbfo3KtJHlnX1B\n/HLXMV1sRYHDV66bh1uuakSF05RWbON7sZhEVLosqK2wodJlgcUklkTHHjE9SRvhbt26FRzHob+/\nH7fccgsWLlw4bKYZjUmfuuQ7Yqe9J5B8/aK2LisqvIEoznX5sX33SfiCMQCA02bEXRua0VDjgNtu\nzCotUMode8T0JK3g3nvvvcXaBzEB5DulNlmpFjBYXxuM4sMTPXjh7bOQBysMGqod2LKhCW6HCW6H\nCUIOlp6l2rFHTE+yGiJJEHHcDiM6ekL643ipltNlQK8vgr+804b9LV36z1csrMItaxphtxhQZjNS\n/TYxraHarhIg33E+Ow+e0yY3RGTYzGLGyQ0AYDMbIfBhqGyoHpbntMqBX/35GM5e0AppBZ7DzVc1\nYtXiatgthoz5WoKYDpDgTnHyHV+z8+A57Nx/DoDWSRgISfrjdKIbkxVUlJnhD0uQZAUCz8Nk4NHR\nE9RLvuwWA7ZsaMKcWidcNhNMRirjIgggyyoFYvKy70gnIiPMZCJZGLzsPdwBRWWIySqikoKYrEJR\nGfYe7kh7X6XLArNJRIXTjEqXFRaTCF9Q0sV2VqUN37ptCebPLEOF00xiSxAJkOBOcdq6/fD4o5Bl\nFWCawYvHH8X57uTVBHEGAjFdJOMoqtZ+m441l9ZAUVTIigpfMAaPP4r4s1zRPAPfvOUSVLmtKC8z\npxyDQxDTlYL+jfj444+xdetWAEBbWxvuvPNObNmyBT/4wQ/0ab9PPfUUvvKVr+COO+7AkSNHCrmd\nkkSSk09NjmUweGEpDAlSrQOAoqqYNcOO+XVl6PGGERhs0eUA3LS6AV++dh7K7EaazEAQKShYDvfp\np5/GSy+9BIvFAgD44Q9/iPvuuw+rVq3Cww8/jD179mDmzJl499138fvf/x6dnZ2499578fzzzxdq\nSyWJIUWXV8Y6Vw5AMm1NIZQxSYE3GMOhT7vw9sedenTMc4DbYUaV2wKX3QSLiY4FCCIVBYtw6+vr\n8eSTT+qPjx49qpeZXXPNNThw4AA++OADrF27FhzHYebMmVAUBf39/YXaUknSUO3QptiKPMABosjD\n5TBlbH+1mZJXDdjMowUzFNEsFVvO9uHFt1t1sRUFHjNcFpiMAj4500diSxAZKNjfkE2bNqG9vV1/\nzBjTazBtNhv8fj8CgQBcLpd+TXy9vLw87XO73VaIYzQwqax0jOn+8WKs+7jp6nnY9udP4bQZR62n\ne+7yMjMCESnpevw+xhi8/ih4Bhx4vx0v72vVr7OaRJSXaU0MosDBF5bG5TMtld+XUtkDQPsYyVj2\nUbSQhE/oLgoGg3A6nbDb7QgGg8PWHY7Mb8bjCWW8Jh25dlYVivHYx+xyC25d0ziq/XV2uSXtc/Mc\nUGYzwh+SoDIGnuPgsBrAg0NPj19v0Q2GZfx+72l8es6j3+uwDtbVMk2UZQWocBrH/F5K6felFPZA\n+0i/j3yEt2iCu3jxYhw6dAirVq3CW2+9hc997nOor6/Hj3/8Y3zjG99AV1cXVFXNGN0So8mn/bXS\nZcFFT1hv0VUZgySrqHSZ9RbdXm8E23afwMVBVzGzUcDVl9XiaKsHPM9B4IfyvWQoQxCZKZrgfve7\n38VDDz2Exx9/HHPnzsWmTZsgCAKWL1+O22+/Haqq4uGHHy7WdqY9gbCEYETWHzMAwYgMbyAKTyCK\nU+1e/Pa1U4jEtGqHSpcFWzc1o9JlwcJ6Fw59epEMZQgiRziWrg5okkJfXcfO//fYXsSSlJSJPHDD\ninq88u55vXV3UYMbm9fNh9Uswu0w5VVfm037Mf2+TK490D7S72NSpxSIyYWkqPq03cR/cWUV+Muh\n8/rj66+YheuvrIPZIMBlN+nOYLmQb/sxQZQaJLgFIl9DmXzvyxWDwCeNcOMYDTw2r5uPxY3lsJhE\nOK2GvJ2+8p2fRhClBgluAcg3oitmJDhvlhPH2rxJf1bhNOPujc2oLrfCaTXAah6b01e+89MIotSg\nZvcCkC6iK8R9uSIrKowGAWbD6N9+u8WAv7ltCWoqrHA7TGMWW0A7cEu+nnl+GkGUEiS4BSDfiK4Y\nkWBUUtDvi6DXGwY3YvKCZhJugMNi0Jy+DOPj9JXv/DSCKDUopVAA8p2Im+992eZ9gxEJgZAEbzCG\nfn8U4ahW8sUBcDlMgwMbzSgvM4+r+QzNJiMIDRLcArB2ae2wXGzi+njfl03eV2Wa7WJUUtDW5cez\nr57UxVbgOVS6zOB5HjwPXH9lXUGcvmg2GUGQ4BaEfCO6fO7bd6QT4aiMQFiCrKgQBR52i0GvAJBk\nrUVXURneO34RL+0bMp9xO0yQFQV9/ihsZhHXX1FHokgQBYQEt0CMPaLLrh+lrdsPrz+qP5ZlFV5/\nFG0ch3BUhi8Yg6So2HWwDYc+7davW9zoRmdfEJLMwDEgJqnYd6QTjTUOEl2CKBB0aDaJiKcHuj1h\nqGwoPdDS2pfynmQG5IwxRGMyBoIx+EIx/NeuY7rYigKHr1w3D+HBfK6iqOA4ThfqXQfOFertEcS0\nhyLcScS+I50YCERHOXilaxAYaUAe79QWBR4dPQFs330SA0FtbI7TZsRdG5oxu8qOXQfP6ZN3ZVUB\nB4DnObT3BEEQRGEgwZ1EnGz3DpspFj/sOtU+kPKehmoHwDA0RVfgYTOLMBsF/OdLRyErTL9uy4Ym\nOKxG2MwiGAPUeHDMAYwBisKgCFPOWoMgpgwkuJOIUFhOuh4MjzYKj7N2aS26PWEYDQIUlYExBl8w\nhj7fUF53xcIq3LKmEQaBh9NmhMUkwiDwiGL03LNUI3sIghg7JLgjKJaXQTJUxpIelalpgs5LGsvx\n9scX8NGpXkgKGzaqTOA53HxVI1YtrgbPc3DbjfqsM6tFRDgm6364HKelFLLpLJvIz4ggpjIkuAlM\ntKuVxSjCnySatZiSd3zJioo/vnUGH53q1XO3cbE1ijy+duNCNNY4YRB4uBxGCAmdZQ3VDjCm+eIq\nCoMgcLBbDBlnoU30Z0QQUxn6/phAsbwMUlFmTx5dltlGr8dbdA+2dEFlDInFChwAk4FHY40TZqOA\ncqdpmNgC+bfbTvRnRBBTGYpwE5hoVytJYeC54SkEngOkEanWQFhCICxBVdlgRcPQzzgOEDggIimw\nWwZnj6WAG/E/2fSXTfRnRBBTGYpwE5hoV6tQWAZjmvDFfzGmjSkHtByvxx9FICwhEpOxbfeJUeIs\ncADHcRnFdt+RTphNIipdFsyusqPSZYHZJGaMVCf6MyKIqQwJbgIT7WolKSoYMOqXJKuQFRX9AxFE\nJQUXvWH89IUWnDg/5Gcr8NohWdwkfN0VdWlfK99IdaI/I4KYylBKIYGJdrVKNV5OVVX0+SJgDDjW\n5sGO108jOphnqCm3Yn6dE5+c6UM4KsNmMWDdFXW4eXVj2tfK15lsoj8jgpjKkOCOYCJdrVLNC+M4\nDqrK8MbhDux5v12vRFgytxxfuXYejAYBX75mHpw2Y9ZjcPJ1NAPI+Ysg8oUEt0DkU6uazBchvv7s\nq6dw9Fw/AC23u3HlbFxz2Uxwg+2/thwnMyRGqt5gDNVuqqcliEJDglsA8q1VVZQUKQUGXWzNRgG3\nXz8fC+rd4DjAZTPBZMxvMkM8Up0sI6gJotQhwS0AhZpSW+kyY+vGBZjhskDgObgdJohC8c89qdOM\nIPKDBLcA5FMBoDIGq1lEMJLcT2FRgxv/e908mI0ijCIPl92UMudbSKjTjCDyh8rCCkCutarxkq+r\nLq1BMg2dO9OBuzY2w2wUYTWJcDsmRmwB6jQjiLFAEW4ByKUCIByV4QvFwBiwrKkS7xzthj805Kew\ndF4F7ljfBA6Aw2qE1Tyxv2XpovexpBpaWvvw3l9OoL3bR2kKomQhwS0A2dSqMsbg9Ud1c/CzF3z4\n7Wsn9ZRCudOErRsXoLrcCp4DXHYTjOM0tnwspKrfNRr4vFMN8TSFQeSHTbrI5l6CmEqQ4BaIdLWq\nsqJiIBCDg+PBGMM7n3Zj14E2qIOND011ZbhjfRMsJhGiwMFln5jDsWSkit6Romkjm4PCQh0yEsRk\ngwQ3C8bzVD4yOGuMMa2+9o9vncUHJ3r0n1e7LWi9MIBHfvM+DAKHFYuq8H9uvmS83sqYSRW9v/BW\nEhFGdqY2ZIhDTBdIcDMwXqfyjGnOXqGoljLwBWN4euenaL3gAwAYBB6zKm041zVUDysrDAdbusFx\nHL5x0+LxeDvjQrLofd+RzrxahbVr8mszJoipxuT4njqJ2XekE5GojB5vGJ19QfR4w4hE5Yyn8i2t\nffj5iy145Dfv4ad/+gQHj3bpYnu+24+fvPCJLrYuuxH/7xcuQUdPQL8/sQbhvWMXx/19jTdjMbUh\nQxxiukARbgbauv3oG4gMTbiVVcRiSlrPgsSoWFUZLvSGcKH3PDatnI2BQAwv7muFMuirOKfWiTtv\naILdYoCU0GmWmBGVlOQtv5OJsZjaxK95/0QvPuv2kyEOUbKQ4GYgFJF1cQQGp9syhmAk9WDHfUc6\nwRiDypg+GZcxhhf3taI/YbjjuivrcP2ymRB4HiLPQeCBZNoqTFDNba6MxdRmyZwKrFvZSC3GRElD\ngpuBWApDmVTrAHDRE0IwIiEYlqGoKniOg6IyfWS5wHP44tVzsGH1HPT3B2EyCCizG+F2mNA7EB31\nfC67cXzeDEEQEwoJbgZEnoMicEPTbaHZKIopos6YpKUbfAGtvpYxhmhChOy0GnDXxmbMrnIAAKxm\nEU6rJqgOqxHhqIJQRNZfy2oW4bCaCvgOCYIoFiS4GairtKO10wdeGC6wdVWjp9uGIpLWJTaor6rK\nkGgAZjLw+JsvXQqn1QgOWjNDKCFbW+myJB2JTqf1BFEaUJVCBm66qkFz5RJ5gANEkYfbYcJNqxv0\naxhjGAhE4QtJYABisgJw3DCxNQgcZpSZ4bQatc4xhwm2ETPH6LSeIEobEtwMLJlTgTVLa2E2CuCg\n+dGuSThBlxVt/E04po28CUVk9Pmiw8zE49kHo0GAyHMod5phStKmu2ROBa5cUAl/KIbOviD8oRiu\nXFBJp/UEUSJQSiEDLa19+OBEDxxWIxyDudYPTvSgscaBplkuDASjehqgqz+Eba+cQCQ2NNdc4AF+\nsISMA1BeZtYf5/JaJLoEMfWhCDcDqRoc3viwA57AkNi2nO3Dz//UAo9fqzIQeA4mAw+e5yAIPMrs\nJjBwKcU23WuR9SFBlAYU4WZgZJ8/YwyKyvR1lTG89n479h7u0K9x2Y2wmES9OULgOfA8l/HwizwF\nCKK0KargSpKEBx54AB0dHeB5Ho888ghEUcQDDzwAjuPQ1NSEH/zgB+D5yRN4V7osaOvywx+WIMkK\nBJ6HzSxi5gwbIjEZO14/jePnvQAAjgM+v6oBVW4zdr/XDgAQBU4X3kyHX+QpQBClTVEF980334Qs\ny3juueewf/9+PPHEE5AkCffddx9WrVqFhx9+GHv27MGGDRuKua201FXZ8dGpXrBB+0FFUeELxrCg\n3oWfvtCC3gEt+rSYRNy5vgnz68oAAKLA4+PTvegdiGbdqjqW0eUEQUx+iiq4c+bMgaIoUFUVgUAA\noijio48+wsqVKwEA11xzDfbv3z+pBPezbj8cNiOCYQmKqmptuAKHd45e1P1ra8qtuHtjM8qdWiRq\nMghYfUkNrlqSm1COxY+AIIjJT1EF12q1oqOjA5///Ofh8Xjw85//HO+9957+ldtms8Hvz9xL73Zb\nIYpjm35QWenIeI0kq+gZiMBmFmEzi2CMwReU9CkNAHDFwip89cbF+qhyu8WAMnv2nWEj97Gu0oF1\nKxuzvn+8yObzKAa0j8m1B4D2MZKx7KOogvvrX/8aa9euxXe+8x10dnbiq1/9KiRpyAQmGAzC6XRm\nfB6PJzSmfVRWOjKapMRnjTksBvT5olAHR+IklnxtXDEb114+E8FABCEATpsRMTD0hGOpnzjDPiZi\nBHk2n0cxoH1Mrj3QPtLvIx/hLerplNPphMOhbbKsrAyyLGPx4sU4dOgQAOCtt97C8uXLi7mlpPhC\nMX0qw/KFVZAVFb3esC62HAfcsLwO1y2bBY7jwHPaDDKLaWz/fsVtHbs94WGzvVpa+8bjbREEMcEU\nNcL92te+hgcffBBbtmyBJEm4//77sWTJEjz00EN4/PHHMXfuXGzatKmYWxqGqjJ4A9FRTmDeQFR3\n+uCAGdoAABUtSURBVDIZeNy0ugHLF1YD0KoQ3A4ThHGorKDZXgRR2hRVcG02G/7jP/5j1Pr27duL\nuY2kxCQF3mAM6mAnA2MM+z7pxF8OndfnI5qNAubOdOg52ritYrpmhlygOlyCKG2o8QFAMCIhMGg8\nA2iHZS+8dRYfne7Vr7FbDHBYDfAGJLzy7mcwGQSsXFQ9rvugOlyCKG0mT4fBBKAyBo8/Cn+C2HoD\nUfznS0d1seU5LbINRSR09oXQ1RdEMBzDhyd7Uj9xnpBbGEGUNtM2wpVkFQOBKOQEA9rWTh+effUk\nghFt2GO5w4SorCAYlvVrVAb4ghJOtQ+M+56oDpcgSptpKbihiIR+X0SPahljeOfTbuw60KY3MzTV\nleH265vwr898MOzeeLY2GE4902wsjGUuGEEQk5tpJbiMMfhCEmLgdLGVFRUv7WvF+yeGUgRXL63F\nppX14HlOF2D9ORKeiyAIIhemjeDKiqqXd1lsWpWBLxTDM7tP4rOLAQCAQeDxpWvn4rL5M/T7zEZR\nTzEkYh5jzS1BENOPaaEaUUnBQIJ3LQCc7/bjmVdPajPIoFkq3rVxAWbNsOnX2C0GuB3GpIJbZjOM\nWiMIgkhHyQtuICwhMCLfuv/jC3j2leNQBhV4Tq0Dd97QDPvgjDEOWpuuxSQikERsASAYUZKuEwRB\npKJkBVdlDAOBGKLSkDAqqopdB9rwzqfd+trqS2pw4+p6vVOM5wC3wwTDoDlOKCwjWVtDoQ7NCIIo\nXUpScGVFhdc/vOQrEJbw29dOorVTM54QeA5fWDsHyxdW6deIPAeXwwRRGCpPHnloNrReoM0TBFGy\nlJzghqMyfMEYEvXwQm8Q23efgDeguXiV2U3YcsN8zK4acvsxijxcDtOoNl2nzQjfYMsvg5Zu4HkO\nTsrhEgSRIyUjuPGSr3B0eM71o9O9eOHNs5AUzZCmvtqOb/3vy6HEhq6zmEQ4rQbdlzeR65bNws79\n58AL3Kh1giCIXCgJwU0s+YqjqgyvvHsebyc4cC1fWIVb1zSizG5Cf78muE6rAVZz6mj15tWN6O4P\n4b1jFyEpKgwCjxWLqnDz6saCvR+CIEqTKS+4caPwxFRrKCLjd6+f0ttveY7DzVc1YNXiaj2K5TjA\nZTfBZEg/OaKltQ/tPUHUJpSLtfcE0dLaRx1hBEHkxJQVXMYY/CEJoREphK7+ELa/cgL9/igAwGYW\nsWVDM+bUOnGq3Yv3j1+EPyxhRpkZ11w2M6No7jvSiUhUhj8sQVZUiAIPh8VAHrUEQeTMlBRcWVEx\nEIjpedk4La39+MMbp3UD8ZkzbLh7YzNcdhNOtXvxyrufgRt0/+rxRvQJuemEs63bD8+geAOALKvw\n+KNJ870EQRDpmJKC2++LDCvLUhnDng/a8caHHfra5fNn4LZr5sIgaiVe7x+/CI7TysE0sdSeIFOk\nKo2Y/hAnJlPjA0EQuTElBTdRbCMxGTteP4Pj5z0AtNzs51c1YM2lNcOi0IFgTItOwxIUhUEQODgs\nhozTFAwiD5WxUWVhhjFODSYIYvoxJQU3To83jO27T+iiaTEJuGN9E5rqXPo1epuuUURXnzbtl+M4\nPTXgcqQfae6yGdEXF+W44jLARXW4BEHkyJQV3OPnPfjdntN6625NuRV3b2xGuXNoHA0/WIlgNAgA\nUrSGZbRZ5MDzHPiRDb6UwyUIIkempODuPdyBV9/7TJfQJXPK8eXr5g0r8RIFDi77UJtuTFZhMYsI\nhCSoTAXPcbBbDYjJ6QU3JitwO0yjqhRiUvLcLkEQRCqm5Eyz3YNiywHYsHw27ryhaZjYmgwCyp3m\nYZ4IRpFHMCzpxuGMMQTDEoxi+ki10mVBRFIQjSmQZBXRmIKIpNBgR4IgcmZKRriAJqq3r5+PhfXu\nYes2swiH1Tjq+mBEhhLvROO0TIKisKRet4kYRB6+QQ8GQKuI8AVievUDQRBEtkxJwZ0/qwy3rGlE\npcuiryV62CbD449CELihagNOqzZIrLFNxrE2D3ie071zAa207FibZzzeCkEQ04gpKbh/fdOiYY95\nnoPbbsxYqsVzHHhBq8PNdiZZ3CksMfGgqgz+IPnhEgSRG1P+e7FB4FHhNGUU27pKG1SVQVZUxGQF\nsqJCVRnqKm1p7xtp1xiHihQIgsiVKS24ZqOAcqdJn9aQjkvmVmDU6AZucD0NVosIBoz6ZbVQHS5B\nELkxJVMKgDbg0Z6D6LVfDKDCaYY/LEFRGQRe6zRrH5zYm4pql2UwrTC0xvNANVUpEASRI1NScMvS\nHI6loscbhtkkwmwSYRB53SMhU2svwEHk+dHfBSinQBBEjkzJlEKuYgtgWEXD8PX0kWq88UEUeYAD\nRJGH22GixgeCIHJmSgpuPqxdWpvTepxKlwVmk4hKlwW1FbaEx5RSIAgiN6ZkSiEf4haM+450whuM\nodptwdqltRlNxNcurcX23ScRSGjttVsMGYWaIAhiJNNGcAFNdJfMqUBlpQM9Pf6s70tS3EAQBJEz\n00pw82HfkU79sG3kOo3YIQgiF6aV4La09mHfkU54AjG47casUgo93nCK9UzVDQRBEMOZNoLb0tqn\nzzAziDy6PeGsZppVuixo6/KPsmdsqHEUZd8EQZQO06ZKYd+RToSjMnq8YXzWHUCPN4xwVMa+I51p\n76urssPjj0KWVYANDZGsq7IXaecEQZQK0ybCbev2wzvoDBYfseP1R9GWoYGh/WIALodpVJVCpg41\ngiCIkUwbwU01fVfKMH23xxuGxSSOaragHC5BELkybVIKBpFP6hZmzOAylm+HGkEQxEimjeC6bMak\nBbVlGabvrl1aq+d+O/uCeu6XGh8IgsiVoqcU/vM//xOvv/46JEnCnXfeiZUrV+KBBx4Ax3FoamrC\nD37wA/BZ2C3mDpfcgDwLExpqfCAIYjwoaoR76NAhHD58GL/97W+xbds2dHV14Yc//CHuu+8+PPvs\ns2CMYc+ePf9/e3cfFFW5xwH8uy67ggsEDuiYgLqWTeVooSh4FSOnSwVJYzJKGhWOAeNEloBlA+Kw\nY5lKE1aDBDnGm+4wZjNdM5teYExiHBDE9a3i5Y5vCIgDi8jL7nP/8HJkATFl9+Dsfj9/sed54Pz2\nOef8OPvsOb9jk3XfbxGaO13FcLerG4iIBpI14R49ehQzZszAunXrEBcXh2eeeQYGgwHz5s0DAAQH\nB+PYsWM2WXf/IjS+E1z/cRGahsb2IS8L+28jr1Igonsj65RCa2srLl26hKysLFy4cAHx8fEQQkDx\n/4/1Go0G7e13r3Hg6TkOTnf5smugsEXTkXfotPS676m7YYumw9v7zjcxmMyQ4rNcLob9vX/KGn/D\nGhiHpQchjgchBoBxDDSSOGRNuB4eHtBqtVCr1dBqtRg7diyuXLkitXd0dMDd3f2uf6e19cY9r9t3\nvAuW/muqVC3MQ3Pr1l7f8S7DFrJRjsGQD5wcM0ZxTwVwhnKvRXRshXE8eHE8CDEwjuHjuJ/EK2vC\nnTNnDr755hu8+eabuHr1Kjo7OxEUFITy8nLMnz8fpaWlCAwMtNn676da2JSJbhACg258mDKRd5oR\n0b2RNeGGhITg+PHjWL58OYQQSE1NhY+PD1JSUpCRkQGtVovQ0FA5Q7qrhbMmobG1c9CND7wsjIju\nleyXhSUnJw9alp+fL3cY/1j/wuVN12/C28P5H1UZIyIayGFu7R2JvqkIIqKRcKiEez/1cImIrMVh\nEu6puhYUHDmP9s4emEwCF5UKNFxpx6p/z2DSJSJZOEwthf8ca7h9AwNu38Dwn7KGUY6MiByFwyTc\nC01D3xnGurZEJBeHSbhERKPNYRKuj7fmnpYTEVmbwyTcsAVT4eLsBJNZoLvXBJNZwMXZCWELpo52\naETkIBzmKgUAcFYp0aM2w2QWUI5RwFl1bwVwiIhGwmES7tGTl+E81gnOY52gchojPePs6MnLvCyM\niGThMFMKTdc777CcD4MkInk4TMLlwyCJaLQ5TMK9U3UvVv0iIrk4zBxu/6pf1zu6MdHThbUUiEhW\nDpNwgfsrQE5EZC0OM6VARDTamHCJiGTChEtEJBMmXCIimTDhEhHJhAmXiEgmTLhERDJhwiUikgkT\nLhGRTBRCCDHaQRAROQKe4RIRyYQJl4hIJky4REQyYcIlIpIJEy4RkUyYcImIZGLXBcjNZjPS0tJw\n7tw5qNVq6HQ6TJkyRWrX6/XYt28fnJycEB8fj5CQEJvE0dPTg02bNuHixYvo7u5GfHw8lixZIrXv\n2bMHxcXFGD9+PABgy5Yt0Gq1Nonl5ZdfhpubGwDAx8cHH330kdQmx3gcOHAA3377LQCgq6sLZ86c\nwe+//w53d3cAgE6nQ2VlJTQaDQDgyy+/lOK1lurqauzYsQN5eXloaGjA+++/D4VCgUcffRSbN2/G\nmDG3z0Nu3ryJpKQktLS0QKPRYNu2bdJ2smYcZ86cQXp6OpRKJdRqNbZt2wYvLy+L/sNtO2vFYTAY\nEBcXh6lTpwIAoqKi8OKLL0p9bTUe/WN499130dzcDAC4ePEiZs+ejU8//VTqK4RAcHCwFONTTz2F\nDRs2jGj9Qx2jjzzyiPX3DWHHfvzxR7Fx40YhhBAnTpwQcXFxUtvVq1dFeHi46OrqEm1tbdLPtlBc\nXCx0Op0QQohr166JxYsXW7Rv2LBB1NTU2GTd/d28eVNEREQM2SbnePRJS0sT+/bts1i2cuVK0dLS\nYrN1Zmdni/DwcBEZGSmEECI2Nlb88ccfQgghUlJSxJEjRyz6f/311yIzM1MIIcT3338v0tPTbRLH\nqlWrxOnTp4UQQhQVFYmtW7da9B9u21kzDr1eL3Jzc+/Y3xbjMTCGPtevXxdLly4VjY2NFsvr6+tF\nbGzsiNfb31DHqC32DbueUqioqMCiRYsA3PoveOrUKant5MmTePrpp6FWq+Hm5gY/Pz+cPXvWJnE8\n//zzeOedd6TXSqXSot1gMCA7OxtRUVHYvXu3TWIAgLNnz6KzsxMxMTGIjo5GVVWV1CbneABATU0N\n/vrrL6xYsUJaZjab0dDQgNTUVKxcuRLFxcVWX6+fnx927dolvTYYDJg3bx4AIDg4GMeOHbPo338f\nCg4ORllZmU3iyMjIwOOPPw4AMJlMGDt2rEX/4badNeM4deoUfvvtN6xatQqbNm2C0Wi06G+L8RgY\nQ59du3Zh9erVmDBhgsVyg8GAxsZGvPbaa1i7di1qa2tHHMNQx6gt9g27TrhGoxGurq7Sa6VSid7e\nXqmt/0dVjUYzaOeyFo1GA1dXVxiNRiQkJGD9+vUW7WFhYUhLS8PevXtRUVGBX3/91SZxODs7Y82a\nNcjNzcWWLVuQmJg4KuMBALt378a6desslt24cQOrV6/G9u3bkZOTg8LCQqsn/dDQUDg53Z5JE0JA\noVAAuPWe29stn3XXf1yGardWHH1JpbKyEvn5+XjjjTcs+g+37awZx6xZs5CcnIyCggL4+vriiy++\nsOhvi/EYGAMAtLS0oKysDMuWLRvU39vbG2+99Rby8vIQGxuLpKSkEccw1DFqi33DrhOuq6srOjo6\npNdms1nasAPbOjo6rD5X2N/ly5cRHR2NiIgIvPTSS9JyIQRef/11jB8/Hmq1GosXL8bp06dtEsO0\nadOwdOlSKBQKTJs2DR4eHmhqagIg73i0tbWhtrYWgYGBFstdXFwQHR0NFxcXuLq6IjAw0KZn2QAs\n5uQ6OjqkueQ+/cdlqHZrOnToEDZv3ozs7OxBc4HDbTtreu655zBz5kzp54H7olzjcfjwYYSHhw/6\nNAgAM2fOlL4DmTt3LhobGyGsUKFg4DFqi33DrhOuv78/SktLAQBVVVWYMWOG1DZr1ixUVFSgq6sL\n7e3t+Pvvvy3aram5uRkxMTFISkrC8uXLLdqMRiPCw8PR0dEBIQTKy8ulHd7aiouL8fHHHwMAGhsb\nYTQa4e3tDUDe8Th+/DgWLFgwaHl9fT1effVVmEwm9PT0oLKyEk8++aRNYujzxBNPoLy8HABQWlqK\nuXPnWrT7+/ujpKREap8zZ45N4vjuu++Qn5+PvLw8+Pr6DmofbttZ05o1a3Dy5EkAQFlZ2aDxl2s8\nysrKEBwcPGTb559/jr179wK4NdXy8MMPS2ei92uoY9QW+4ZdF6/pu0rh/PnzEEJg69atKC0thZ+f\nH5YsWQK9Xo/9+/dDCIHY2FiEhobaJA6dTocffvjB4sqDyMhIdHZ2YsWKFTh48CDy8vKgVqsRFBSE\nhIQEm8TR3d2NDz74AJcuXYJCoUBiYiKqq6tlH4+cnBw4OTlJH5v37NkjxfDVV1/h8OHDUKlUiIiI\nQFRUlNXXf+HCBbz33nvQ6/Woq6tDSkoKenp6oNVqodPpoFQqERMTg6ysLJhMJmzcuBFNTU1QqVTY\nuXOn1RJdXxxFRUUICgrCpEmTpLOkgIAAJCQkIDk5GevXr4eXl9egbefv72/VOPR6PQwGA9LT06FS\nqeDl5YX09HS4urrafDz6xwDcmmYrKiqyOGvsi6GzsxNJSUm4ceMGlEolUlNTMX369BGtf6hj9MMP\nP4ROp7PqvmHXCZeI6EFi11MKREQPEiZcIiKZMOESEcmECZeISCZMuEREMmHCJSKSCRMuEZFM7Lo8\nIzmm3t5epKWl4c8//0RzczMee+wxZGRkQK/XIz8/H25ubtBqtfDz88Pbb7+N0tJSZGZmore3Fz4+\nPkhPT4enp+dovw2yQzzDJbtz4sQJqFQq7N+/Hz/99BPa29uRk5ODgoICHDhwAIWFhWhoaAAAXLt2\nDTt37kRubi4OHjyIhQsXYseOHaP8Dshe8QyX7E5AQAA8PDxQUFCA2tpa1NfXY/78+QgJCZGqx4WF\nhaGtrQ3V1dVS0RLg1u3gDz300GiGT3aMCZfszs8//4zMzExER0dj2bJlaG1thZubG9ra2gb1NZlM\n8Pf3R1ZWFoBbT6HoXzWNyJo4pUB2p6ysDC+88AJeeeUVuLu7SxWfSkpKYDQa0d3djSNHjkChUGD2\n7NmoqqpCXV0dgFuP9Pnkk09GM3yyYyxeQ3bn3LlzSExMBACoVCpMnjwZWq0WEyZMQGFhIcaNGwdP\nT08EBARg7dq1+OWXX/DZZ5/BbDZj4sSJ2L59O780I5tgwiWHUFdXh5KSEqkkZHx8PCIjI/Hss8+O\nbmDkUDiHSw5h8uTJqKmpQXh4OBQKBRYuXGizpzQT3QnPcImIZMIvzYiIZMKES0QkEyZcIiKZMOES\nEcmECZeISCZMuEREMvkfb+ZNN7JJPlIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114fbd438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x='age',y='height',data=data[data.age < 20],fit_reg=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Linear model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"import numpy as np\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import cross_val_predict"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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VV44ioVF8Ph9lZeWH9wd2DJm2IKWhoYH77ruPW265BYA33niDefPmccUVV1Bb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\nB/zoGZ90avCcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11509c630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 89.62222301721019. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see above the Error is significantly high. Predictions are off quite a bit.\n",
"\n",
"Lets try to help the linear model by adding more features.\n",
"\n",
" * As we see from the data we can probably add a new feature like age < 20"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data['age_less_than_20'] = (data.age<20).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" <th>age_less_than_20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male age_less_than_20\n",
"0 151.765 47.825606 63.0 1 0\n",
"1 139.700 36.485807 63.0 0 0\n",
"2 136.525 31.864838 65.0 0 0\n",
"3 156.845 53.041915 41.0 1 0\n",
"4 145.415 41.276872 51.0 0 0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets try to fit the model again with this new feature"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Lxr+xEEKICRm5FWBUeqgrNx21rYCjuaVUUFCILuIa9fydZpcSn8837GdyM4QF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"text/plain": [
"<matplotlib.figure.Figure at 0x115b89f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 85.68689844692364. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the error decreased, but not a lot. Let go ahead and add a feature which is square of the age and square of the weight"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['squared_age'] = data['age'] ** 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['squared_weight'] = data['weight'] ** 2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" <th>age_less_than_20</th>\n",
" <th>squared_age</th>\n",
" <th>squared_weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3969.0</td>\n",
" <td>2287.288637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3969.0</td>\n",
" <td>1331.214076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4225.0</td>\n",
" <td>1015.367901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1681.0</td>\n",
" <td>2813.444694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2601.0</td>\n",
" <td>1703.780162</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male age_less_than_20 squared_age \\\n",
"0 151.765 47.825606 63.0 1 0 3969.0 \n",
"1 139.700 36.485807 63.0 0 0 3969.0 \n",
"2 136.525 31.864838 65.0 0 0 4225.0 \n",
"3 156.845 53.041915 41.0 1 0 1681.0 \n",
"4 145.415 41.276872 51.0 0 0 2601.0 \n",
"\n",
" squared_weight \n",
"0 2287.288637 \n",
"1 1331.214076 \n",
"2 1015.367901 \n",
"3 2813.444694 \n",
"4 1703.780162 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5TP0nf6Kg4FvxhMyamn3c9MCfsVUkV4v1uVoomLSAYMBDzrjZ5IybTdOWv+Pa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\nEQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115bdfe10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 28.509278178481857. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! We were able to reduce the error from 89 to 28.5 by adding higher order features. One thing to note is that the model complexity increase as we add more higher order features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random forest\n",
"\n",
"We can use a random forest classifier which can fit the data without even needing any higher order features"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('dataset/Howell1.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male\n",
"0 151.765 47.825606 63.0 1\n",
"1 139.700 36.485807 63.0 0\n",
"2 136.525 31.864838 65.0 0\n",
"3 156.845 53.041915 41.0 1\n",
"4 145.415 41.276872 51.0 0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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TUyO9ziA2SyBlSkartxINB9NO1ZiyiyEGezb9AZOtDGNWEd4BBwFvPyVzzqKn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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115b259e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 24.180449739870596. (Lower the better)\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"lr = RandomForestRegressor()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The lowest mean squared error without using any higher order features"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" 'n_estimators': [3, 5, 10, 20, 50], \n",
" 'max_depth': [3, 5, 7, 9],\n",
" 'min_samples_leaf' : [1, 2, 3, 4, 5]\n",
"}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use grid_search_cv to find the best parameters"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n",
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.model_selection import StratifiedKFold, KFold"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"grid = GridSearchCV(estimator=RandomForestRegressor(), param_grid=params, cv=5, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 10.4s finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise',\n",
" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid={'n_estimators': [3, 5, 10, 20, 50], 'min_samples_leaf': [1, 2, 3, 4, 5], 'max_depth': [3, 5, 7, 9]},\n",
" pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.fit(train, target)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=5, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 5, 'min_samples_leaf': 5, 'n_estimators': 50}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9633401674741805"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the best params from the grid search above"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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rWUkRQgjxicR7oLz0+tuoCxaRlxOkraEisS0ST3pVaXQp5+vEgxa9yZZoax+L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\nJU+WGCkA0QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115060898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 19.702636480588406. (Lower the better)\n"
]
}
],
"source": [
"lr = RandomForestRegressor(n_estimators=20, min_samples_leaf=2, max_depth=5)\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With random forest we are able to get the lowest mean squared error and able to predict the height within +- 20 cms of the true value"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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